ArticleSensors (Basel, Switzerland)2022
Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene.Polymers · 2025Article
- Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization.Scientific reports · 2024Article
- Development of an Open-Source Injection Mold Monitoring System.Sensors (Basel, Switzerland) · 2023Article
- Online Prediction of Molded Part Quality in the Injection Molding Process Using High-Resolution Time Series.Polymers · 2023Article
- Multiple In-Mold Sensors for Quality and Process Control in Injection Molding.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in multidimensional spaces by learning the structure of datasets. In this study, we used four ML algorithms (kNN, naïve Bayes, linear discriminant analysis, and decision tree) and compared their effectiveness in predicting the quality of multi-cavity injection molding. We used pressure-based quality indexes (features) as inputs for the classification algorithms. We proved that all the examined ML algorithms adequately predict quality in injection molding even with very little training data. We found that the decision tree algorithm was the most accurate one, with a computational time of only 8-10 s. The average performance of the decision tree algorithm exceeded 90%, even for very little training data. We also demonstrated that feature selection does not significantly affect the accuracy of the decision tree algorithm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.